339 lines
15 KiB
Markdown
339 lines
15 KiB
Markdown
# Design Amazon's sales rank by category feature
|
|
|
|
*Note: This document links directly to relevant areas found in the [system design topics](https://github.com/donnemartin/system-design-primer#index-of-system-design-topics) to avoid duplication. Refer to the linked content for general talking points, tradeoffs, and alternatives.*
|
|
|
|
## Step 1: Outline use cases and constraints
|
|
|
|
> Gather requirements and scope the problem.
|
|
> Ask questions to clarify use cases and constraints.
|
|
> Discuss assumptions.
|
|
|
|
Without an interviewer to address clarifying questions, we'll define some use cases and constraints.
|
|
|
|
### Use cases
|
|
|
|
#### We'll scope the problem to handle only the following use case
|
|
|
|
* **Service** calculates the past week's most popular products by category
|
|
* **User** views the past week's most popular products by category
|
|
* **Service** has high availability
|
|
|
|
#### Out of scope
|
|
|
|
* The general e-commerce site
|
|
* Design components only for calculating sales rank
|
|
|
|
### Constraints and assumptions
|
|
|
|
#### State assumptions
|
|
|
|
* Traffic is not evenly distributed
|
|
* Items can be in multiple categories
|
|
* Items cannot change categories
|
|
* There are no subcategories ie `foo/bar/baz`
|
|
* Results must be updated hourly
|
|
* More popular products might need to be updated more frequently
|
|
* 10 million products
|
|
* 1000 categories
|
|
* 1 billion transactions per month
|
|
* 100 billion read requests per month
|
|
* 100:1 read to write ratio
|
|
|
|
#### Calculate usage
|
|
|
|
**Clarify with your interviewer if you should run back-of-the-envelope usage calculations.**
|
|
|
|
* Size per transaction:
|
|
* `created_at` - 5 bytes
|
|
* `product_id` - 8 bytes
|
|
* `category_id` - 4 bytes
|
|
* `seller_id` - 8 bytes
|
|
* `buyer_id` - 8 bytes
|
|
* `quantity` - 4 bytes
|
|
* `total_price` - 5 bytes
|
|
* Total: ~40 bytes
|
|
* 40 GB of new transaction content per month
|
|
* 40 bytes per transaction * 1 billion transactions per month
|
|
* 1.44 TB of new transaction content in 3 years
|
|
* Assume most are new transactions instead of updates to existing ones
|
|
* 400 transactions per second on average
|
|
* 40,000 read requests per second on average
|
|
|
|
Handy conversion guide:
|
|
|
|
* 2.5 million seconds per month
|
|
* 1 request per second = 2.5 million requests per month
|
|
* 40 requests per second = 100 million requests per month
|
|
* 400 requests per second = 1 billion requests per month
|
|
|
|
## Step 2: Create a high level design
|
|
|
|
> Outline a high level design with all important components.
|
|
|
|
|
|
|
|
## Step 3: Design core components
|
|
|
|
> Dive into details for each core component.
|
|
|
|
### Use case: Service calculates the past week's most popular products by category
|
|
|
|
We could store the raw **Sales API** server log files on a managed **Object Store** such as Amazon S3, rather than managing our own distributed file system.
|
|
|
|
**Clarify with your interviewer how much code you are expected to write**.
|
|
|
|
We'll assume this is a sample log entry, tab delimited:
|
|
|
|
```
|
|
timestamp product_id category_id qty total_price seller_id buyer_id
|
|
t1 product1 category1 2 20.00 1 1
|
|
t2 product1 category2 2 20.00 2 2
|
|
t2 product1 category2 1 10.00 2 3
|
|
t3 product2 category1 3 7.00 3 4
|
|
t4 product3 category2 7 2.00 4 5
|
|
t5 product4 category1 1 5.00 5 6
|
|
...
|
|
```
|
|
|
|
The **Sales Rank Service** could use **MapReduce**, using the **Sales API** server log files as input and writing the results to an aggregate table `sales_rank` in a **SQL Database**. We should discuss the [use cases and tradeoffs between choosing SQL or NoSQL](https://github.com/donnemartin/system-design-primer#sql-or-nosql).
|
|
|
|
We'll use a multi-step **MapReduce**:
|
|
|
|
* **Step 1** - Transform the data to `(category, product_id), sum(quantity)`
|
|
* **Step 2** - Perform a distributed sort
|
|
|
|
```
|
|
class SalesRanker(MRJob):
|
|
|
|
def within_past_week(self, timestamp):
|
|
"""Return True if timestamp is within past week, False otherwise."""
|
|
...
|
|
|
|
def mapper(self, _ line):
|
|
"""Parse each log line, extract and transform relevant lines.
|
|
|
|
Emit key value pairs of the form:
|
|
|
|
(category1, product1), 2
|
|
(category2, product1), 2
|
|
(category2, product1), 1
|
|
(category1, product2), 3
|
|
(category2, product3), 7
|
|
(category1, product4), 1
|
|
"""
|
|
timestamp, product_id, category_id, quantity, total_price, seller_id, \
|
|
buyer_id = line.split('\t')
|
|
if self.within_past_week(timestamp):
|
|
yield (category_id, product_id), quantity
|
|
|
|
def reducer(self, key, value):
|
|
"""Sum values for each key.
|
|
|
|
(category1, product1), 2
|
|
(category2, product1), 3
|
|
(category1, product2), 3
|
|
(category2, product3), 7
|
|
(category1, product4), 1
|
|
"""
|
|
yield key, sum(values)
|
|
|
|
def mapper_sort(self, key, value):
|
|
"""Construct key to ensure proper sorting.
|
|
|
|
Transform key and value to the form:
|
|
|
|
(category1, 2), product1
|
|
(category2, 3), product1
|
|
(category1, 3), product2
|
|
(category2, 7), product3
|
|
(category1, 1), product4
|
|
|
|
The shuffle/sort step of MapReduce will then do a
|
|
distributed sort on the keys, resulting in:
|
|
|
|
(category1, 1), product4
|
|
(category1, 2), product1
|
|
(category1, 3), product2
|
|
(category2, 3), product1
|
|
(category2, 7), product3
|
|
"""
|
|
category_id, product_id = key
|
|
quantity = value
|
|
yield (category_id, quantity), product_id
|
|
|
|
def reducer_identity(self, key, value):
|
|
yield key, value
|
|
|
|
def steps(self):
|
|
"""Run the map and reduce steps."""
|
|
return [
|
|
self.mr(mapper=self.mapper,
|
|
reducer=self.reducer),
|
|
self.mr(mapper=self.mapper_sort,
|
|
reducer=self.reducer_identity),
|
|
]
|
|
```
|
|
|
|
The result would be the following sorted list, which we could insert into the `sales_rank` table:
|
|
|
|
```
|
|
(category1, 1), product4
|
|
(category1, 2), product1
|
|
(category1, 3), product2
|
|
(category2, 3), product1
|
|
(category2, 7), product3
|
|
```
|
|
|
|
The `sales_rank` table could have the following structure:
|
|
|
|
```
|
|
id int NOT NULL AUTO_INCREMENT
|
|
category_id int NOT NULL
|
|
total_sold int NOT NULL
|
|
product_id int NOT NULL
|
|
PRIMARY KEY(id)
|
|
FOREIGN KEY(category_id) REFERENCES Categories(id)
|
|
FOREIGN KEY(product_id) REFERENCES Products(id)
|
|
```
|
|
|
|
We'll create an [index](https://github.com/donnemartin/system-design-primer#use-good-indices) on `id `, `category_id`, and `product_id` to speed up lookups (log-time instead of scanning the entire table) and to keep the data in memory. Reading 1 MB sequentially from memory takes about 250 microseconds, while reading from SSD takes 4x and from disk takes 80x longer.<sup><a href=https://github.com/donnemartin/system-design-primer#latency-numbers-every-programmer-should-know>1</a></sup>
|
|
|
|
### Use case: User views the past week's most popular products by category
|
|
|
|
* The **Client** sends a request to the **Web Server**, running as a [reverse proxy](https://github.com/donnemartin/system-design-primer#reverse-proxy-web-server)
|
|
* The **Web Server** forwards the request to the **Read API** server
|
|
* The **Read API** server reads from the **SQL Database** `sales_rank` table
|
|
|
|
We'll use a public [**REST API**](https://github.com/donnemartin/system-design-primer#representational-state-transfer-rest):
|
|
|
|
```
|
|
$ curl https://amazon.com/api/v1/popular?category_id=1234
|
|
```
|
|
|
|
Response:
|
|
|
|
```
|
|
{
|
|
"id": "100",
|
|
"category_id": "1234",
|
|
"total_sold": "100000",
|
|
"product_id": "50",
|
|
},
|
|
{
|
|
"id": "53",
|
|
"category_id": "1234",
|
|
"total_sold": "90000",
|
|
"product_id": "200",
|
|
},
|
|
{
|
|
"id": "75",
|
|
"category_id": "1234",
|
|
"total_sold": "80000",
|
|
"product_id": "3",
|
|
},
|
|
```
|
|
|
|
For internal communications, we could use [Remote Procedure Calls](https://github.com/donnemartin/system-design-primer#remote-procedure-call-rpc).
|
|
|
|
## Step 4: Scale the design
|
|
|
|
> Identify and address bottlenecks, given the constraints.
|
|
|
|
|
|
|
|
**Important: Do not simply jump right into the final design from the initial design!**
|
|
|
|
State you would 1) **Benchmark/Load Test**, 2) **Profile** for bottlenecks 3) address bottlenecks while evaluating alternatives and trade-offs, and 4) repeat. See [Design a system that scales to millions of users on AWS](https://github.com/donnemartin/system-design-primer/blob/master/solutions/system_design/scaling_aws/README.md) as a sample on how to iteratively scale the initial design.
|
|
|
|
It's important to discuss what bottlenecks you might encounter with the initial design and how you might address each of them. For example, what issues are addressed by adding a **Load Balancer** with multiple **Web Servers**? **CDN**? **Master-Slave Replicas**? What are the alternatives and **Trade-Offs** for each?
|
|
|
|
We'll introduce some components to complete the design and to address scalability issues. Internal load balancers are not shown to reduce clutter.
|
|
|
|
*To avoid repeating discussions*, refer to the following [system design topics](https://github.com/donnemartin/system-design-primer#index-of-system-design-topics) for main talking points, tradeoffs, and alternatives:
|
|
|
|
* [DNS](https://github.com/donnemartin/system-design-primer#domain-name-system)
|
|
* [CDN](https://github.com/donnemartin/system-design-primer#content-delivery-network)
|
|
* [Load balancer](https://github.com/donnemartin/system-design-primer#load-balancer)
|
|
* [Horizontal scaling](https://github.com/donnemartin/system-design-primer#horizontal-scaling)
|
|
* [Web server (reverse proxy)](https://github.com/donnemartin/system-design-primer#reverse-proxy-web-server)
|
|
* [API server (application layer)](https://github.com/donnemartin/system-design-primer#application-layer)
|
|
* [Cache](https://github.com/donnemartin/system-design-primer#cache)
|
|
* [Relational database management system (RDBMS)](https://github.com/donnemartin/system-design-primer#relational-database-management-system-rdbms)
|
|
* [SQL write master-slave failover](https://github.com/donnemartin/system-design-primer#fail-over)
|
|
* [Master-slave replication](https://github.com/donnemartin/system-design-primer#master-slave-replication)
|
|
* [Consistency patterns](https://github.com/donnemartin/system-design-primer#consistency-patterns)
|
|
* [Availability patterns](https://github.com/donnemartin/system-design-primer#availability-patterns)
|
|
|
|
The **Analytics Database** could use a data warehousing solution such as Amazon Redshift or Google BigQuery.
|
|
|
|
We might only want to store a limited time period of data in the database, while storing the rest in a data warehouse or in an **Object Store**. An **Object Store** such as Amazon S3 can comfortably handle the constraint of 40 GB of new content per month.
|
|
|
|
To address the 40,000 *average* read requests per second (higher at peak), traffic for popular content (and their sales rank) should be handled by the **Memory Cache** instead of the database. The **Memory Cache** is also useful for handling the unevenly distributed traffic and traffic spikes. With the large volume of reads, the **SQL Read Replicas** might not be able to handle the cache misses. We'll probably need to employ additional SQL scaling patterns.
|
|
|
|
400 *average* writes per second (higher at peak) might be tough for a single **SQL Write Master-Slave**, also pointing to a need for additional scaling techniques.
|
|
|
|
SQL scaling patterns include:
|
|
|
|
* [Federation](https://github.com/donnemartin/system-design-primer#federation)
|
|
* [Sharding](https://github.com/donnemartin/system-design-primer#sharding)
|
|
* [Denormalization](https://github.com/donnemartin/system-design-primer#denormalization)
|
|
* [SQL Tuning](https://github.com/donnemartin/system-design-primer#sql-tuning)
|
|
|
|
We should also consider moving some data to a **NoSQL Database**.
|
|
|
|
## Additional talking points
|
|
|
|
> Additional topics to dive into, depending on the problem scope and time remaining.
|
|
|
|
#### NoSQL
|
|
|
|
* [Key-value store](https://github.com/donnemartin/system-design-primer#key-value-store)
|
|
* [Document store](https://github.com/donnemartin/system-design-primer#document-store)
|
|
* [Wide column store](https://github.com/donnemartin/system-design-primer#wide-column-store)
|
|
* [Graph database](https://github.com/donnemartin/system-design-primer#graph-database)
|
|
* [SQL vs NoSQL](https://github.com/donnemartin/system-design-primer#sql-or-nosql)
|
|
|
|
### Caching
|
|
|
|
* Where to cache
|
|
* [Client caching](https://github.com/donnemartin/system-design-primer#client-caching)
|
|
* [CDN caching](https://github.com/donnemartin/system-design-primer#cdn-caching)
|
|
* [Web server caching](https://github.com/donnemartin/system-design-primer#web-server-caching)
|
|
* [Database caching](https://github.com/donnemartin/system-design-primer#database-caching)
|
|
* [Application caching](https://github.com/donnemartin/system-design-primer#application-caching)
|
|
* What to cache
|
|
* [Caching at the database query level](https://github.com/donnemartin/system-design-primer#caching-at-the-database-query-level)
|
|
* [Caching at the object level](https://github.com/donnemartin/system-design-primer#caching-at-the-object-level)
|
|
* When to update the cache
|
|
* [Cache-aside](https://github.com/donnemartin/system-design-primer#cache-aside)
|
|
* [Write-through](https://github.com/donnemartin/system-design-primer#write-through)
|
|
* [Write-behind (write-back)](https://github.com/donnemartin/system-design-primer#write-behind-write-back)
|
|
* [Refresh ahead](https://github.com/donnemartin/system-design-primer#refresh-ahead)
|
|
|
|
### Asynchronism and microservices
|
|
|
|
* [Message queues](https://github.com/donnemartin/system-design-primer#message-queues)
|
|
* [Task queues](https://github.com/donnemartin/system-design-primer#task-queues)
|
|
* [Back pressure](https://github.com/donnemartin/system-design-primer#back-pressure)
|
|
* [Microservices](https://github.com/donnemartin/system-design-primer#microservices)
|
|
|
|
### Communications
|
|
|
|
* Discuss tradeoffs:
|
|
* External communication with clients - [HTTP APIs following REST](https://github.com/donnemartin/system-design-primer#representational-state-transfer-rest)
|
|
* Internal communications - [RPC](https://github.com/donnemartin/system-design-primer#remote-procedure-call-rpc)
|
|
* [Service discovery](https://github.com/donnemartin/system-design-primer#service-discovery)
|
|
|
|
### Security
|
|
|
|
Refer to the [security section](https://github.com/donnemartin/system-design-primer#security).
|
|
|
|
### Latency numbers
|
|
|
|
See [Latency numbers every programmer should know](https://github.com/donnemartin/system-design-primer#latency-numbers-every-programmer-should-know).
|
|
|
|
### Ongoing
|
|
|
|
* Continue benchmarking and monitoring your system to address bottlenecks as they come up
|
|
* Scaling is an iterative process
|